Journal of Integrative Bioinformatics最新文献

{"title":"Super-rapid race for saving lives by developing COVID-19 vaccines.","authors":"Anusha Uttarilli,&nbsp;Sridhar Amalakanti,&nbsp;Phaneeswara-Rao Kommoju,&nbsp;Srihari Sharma,&nbsp;Pankaj Goyal,&nbsp;Gowrang Kasaba Manjunath,&nbsp;Vineet Upadhayay,&nbsp;Alisha Parveen,&nbsp;Ravi Tandon,&nbsp;Kumar Suranjit Prasad,&nbsp;Tikam Chand Dakal,&nbsp;Izhar Ben Shlomo,&nbsp;Malik Yousef,&nbsp;Muniasamy Neerathilingam,&nbsp;Abhishek Kumar","doi":"10.1515/jib-2021-0002","DOIUrl":"https://doi.org/10.1515/jib-2021-0002","url":null,"abstract":"<p><p>The pandemic of coronavirus disease 2019 (COVID-19) caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has affected millions of people and claimed thousands of lives. Starting in China, it is arguably the most precipitous global health calamity of modern times. The entire world has rocked back to fight against the disease and the COVID-19 vaccine is the prime weapon. Even though the conventional vaccine development pipeline usually takes more than a decade, the escalating daily death rates due to COVID-19 infections have resulted in the development of fast-track strategies to bring in the vaccine under a year's time. Governments, companies, and universities have networked to pool resources and have come up with a number of vaccine candidates. Also, international consortia have emerged to address the distribution of successful candidates. Herein, we summarize these unprecedented developments in vaccine science and discuss the types of COVID-19 vaccines, their developmental strategies, and their roles as well as their limitations.</p>","PeriodicalId":53625,"journal":{"name":"Journal of Integrative Bioinformatics","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2021-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1515/jib-2021-0002","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"25525257","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Special issue on COVID-19 data integration opportunities and vaccine development strategies.","authors":"Jens Allmer","doi":"10.1515/jib-2021-0006","DOIUrl":"https://doi.org/10.1515/jib-2021-0006","url":null,"abstract":"Viral infections affect a large part of the human population once or several times each year. Coronaviruses (CoV) are part of the viruses which cause ailments such as the common cold. With SARS-CoV-1, a dangerous variant of CoV caused an epidemic that did not spread worldwide (2002–2004). It has been contained with less than one thousand fatalities (WHO). Another beta coronavirus causing the middle east respiratory syndrome (MERS) broke out about a decade later (2013). While MERS cases are still present in 2021 (most cases reported by Saudi Arabia), the cumulative death toll is below one thousand despite a high case-to-fatality ratio of around 30% [1]. In 2019 SARS-CoV-2 caused a pandemic with abundant worldwide infections and about two million fatalities in early 2021 (http://covid19.who.int). With the SARS-CoV-2 pandemic active for more than one year, vaccines with emergency admittance are being delivered. Interestingly, during 50 years of research on vaccines against coronaviridae, such approaches are only now becoming available (Figure 1). Vaccination of a sufficiently large cohort of individuals to control the pandemic will take long at current vaccination rates. Therefore, it is essential to continue studying SARS-CoV-2 and try additional routes to prevent the virus’s spread or the disease. Yousef et al. state that testing data is fragmented and not readily available [2]. With a relatively large dataset provided by the Israeli government, they trained a machine-learning algorithm that aided in ranking symptoms, allowing testing prioritization. Demirci and Sacar Demirci show how post-transcriptional gene regulation can be involved in the COVID-19 disease and investigate different miRNAs’ targets and their differential expression [3]. Gültekin and Allmer show how novel information such as RNA binding potential and predicted CoV microRNAs could be incorporated into genome browsers [4]. Such data can help RNA-based drug design. Ahsan et al. tie together many resources with CoV’ information ranging from genomic data to clinical trials [5]. Due to the amount of data generated in the last year, such a resource was desperately needed. OverCOVID will help researchers to find the information they need and may enable integrative studies. Finally, Uttarilli et al. discuss the rapid development of COVID-19 vaccines [6]. Thus, this special issue brings together two applications of COVID-19 data, one visualization of such data, a resource potentially delivering data with integration potential, and a review of vaccine development, which could benefit from the resources mentioned above.","PeriodicalId":53625,"journal":{"name":"Journal of Integrative Bioinformatics","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2021-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1515/jib-2021-0006","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"25498300","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"OverCOVID: an integrative web portal for SARS-CoV-2 bioinformatics resources.","authors":"Md Asif Ahsan,&nbsp;Yongjing Liu,&nbsp;Cong Feng,&nbsp;Ralf Hofestädt,&nbsp;Ming Chen","doi":"10.1515/jib-2020-0046","DOIUrl":"https://doi.org/10.1515/jib-2020-0046","url":null,"abstract":"<p><p>Outbreaks of COVID-19 caused by the novel coronavirus SARS-CoV-2 is still a threat to global human health. In order to understand the biology of SARS-CoV-2 and developing drug against COVID-19, a vast amount of genomic, proteomic, interatomic, and clinical data is being generated, and the bioinformatics researchers produced databases, webservers and tools to gather those publicly available data and provide an opportunity of analyzing such data. However, these bioinformatics resources are scattered and researchers need to find them from different resources discretely. To facilitate researchers in finding the resources in one frame, we have developed an integrated web portal called OverCOVID (http://bis.zju.edu.cn/overcovid/). The publicly available webservers, databases and tools associated with SARS-CoV-2 have been incorporated in the resource page. In addition, a network view of the resources is provided to display the scope of the research. Other information like SARS-CoV-2 strains is visualized and various layers of interaction resources is listed in distinct pages of the web portal. As an integrative web portal, the OverCOVID will help the scientist to search the resources and accelerate the clinical research of SARS-CoV-2.</p>","PeriodicalId":53625,"journal":{"name":"Journal of Integrative Bioinformatics","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2021-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1515/jib-2020-0046","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"25491809","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Circular RNA-MicroRNA-MRNA interaction predictions in SARS-CoV-2 infection.","authors":"Yılmaz Mehmet Demirci, Müşerref Duygu Saçar Demirci","doi":"10.1515/jib-2020-0047","DOIUrl":"10.1515/jib-2020-0047","url":null,"abstract":"<p><p>Different types of noncoding RNAs like microRNAs (miRNAs) and circular RNAs (circRNAs) have been shown to take part in various cellular processes including post-transcriptional gene regulation during infection. MiRNAs are expressed by more than 200 organisms ranging from viruses to higher eukaryotes. Since miRNAs seem to be involved in host-pathogen interactions, many studies attempted to identify whether human miRNAs could target severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) mRNAs as an antiviral defence mechanism. In this work, a machine learning based miRNA analysis workflow was developed to predict differential expression patterns of human miRNAs during SARS-CoV-2 infection. In order to obtain the graphical representation of miRNA hairpins, 36 features were defined based on the secondary structures. Moreover, potential targeting interactions between human circRNAs and miRNAs as well as human miRNAs and viral mRNAs were investigated.</p>","PeriodicalId":53625,"journal":{"name":"Journal of Integrative Bioinformatics","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2021-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1515/jib-2020-0047","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"25483358","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Novel perspectives for SARS-CoV-2 genome browsing.","authors":"Visam Gültekin,&nbsp;Jens Allmer","doi":"10.1515/jib-2021-0001","DOIUrl":"https://doi.org/10.1515/jib-2021-0001","url":null,"abstract":"<p><p>SARS-CoV-2 has spread worldwide and caused social, economic, and health turmoil. The first genome assembly of SARS-CoV-2 was produced in Wuhan, and it is widely used as a reference. Subsequently, more than a hundred additional SARS-CoV-2 genomes have been sequenced. While the genomes appear to be mostly identical, there are variations. Therefore, an alignment of all available genomes and the derived consensus sequence could be used as a reference, better serving the science community. Variations are significant, but representing them in a genome browser can become, especially if their sequences are largely identical. Here we summarize the variation in one track. Other information not currently found in genome browsers for SARS-CoV-2, such as predicted miRNAs and predicted TRS as well as secondary structure information, were also added as tracks to the consensus genome. We believe that a genome browser based on the consensus sequence is better suited when considering worldwide effects and can become a valuable resource in the combating of COVID-19. The genome browser is available at http://cov.iaba.online.</p>","PeriodicalId":53625,"journal":{"name":"Journal of Integrative Bioinformatics","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2021-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1515/jib-2021-0001","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"25480621","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Clinical presentation of COVID-19 - a model derived by a machine learning algorithm.","authors":"Malik Yousef, Louise C Showe, Izhar Ben Shlomo","doi":"10.1515/jib-2020-0050","DOIUrl":"10.1515/jib-2020-0050","url":null,"abstract":"<p><p>COVID-19 pandemic has flooded all triage stations, making it difficult to carefully select those most likely infected. Data on total patients tested, infected, and hospitalized is fragmentary making it difficult to easily select those most likely to be infected. The Israeli Ministry of Health made public its registry of immediate clinical data and the respective status of infected/not infected for all viral DNA tests performed up to Apr. 18th, 2020 including almost 120,000 tests. We used a machine-learning algorithm to find out which immediate clinical elements mattered the most in identifying the true status of the tested persons including age or gender matter, to enable future better allocation of surveillance policy for those belonging to high-risk groups. In addition to the analyses applied on the first batch of the available data (Apr. 11th), we further tested the algorithm on the independent second batch (Apr. 12th to 18th). Fever, cough and headache were the most diagnostic, differing in degree of importance in different subgroups. Higher percentage of men were found positive (9.3 vs. 7.3%), but gender did not matter for the clinical presentation. The prediction power of the model was high, with accuracy of 0.84 and area under the curve 0.92. We provide a hand-held short checklist with verbal description of importance for the leading symptoms, which should expedite the triage and enable proper selection of people for further follow-up.</p>","PeriodicalId":53625,"journal":{"name":"Journal of Integrative Bioinformatics","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2021-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8035960/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"25441950","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"BioDWH2: an automated graph-based data warehouse and mapping tool.","authors":"Marcel Friedrichs","doi":"10.1515/jib-2020-0033","DOIUrl":"10.1515/jib-2020-0033","url":null,"abstract":"<p><p>Data integration plays a vital role in scientific research. In biomedical research, the OMICS fields have shown the need for larger datasets, like proteomics, pharmacogenomics, and newer fields like foodomics. As research projects require multiple data sources, mapping between these sources becomes necessary. Utilized workflow systems and integration tools therefore need to process large amounts of heterogeneous data formats, check for data source updates, and find suitable mapping methods to cross-reference entities from different databases. This article presents BioDWH2, an open-source, graph-based data warehouse and mapping tool, capable of helping researchers with these issues. A workspace centered approach allows project-specific data source selections and Neo4j or GraphQL server tools enable quick access to the database for analysis. The BioDWH2 tools are available to the scientific community at https://github.com/BioDWH2.</p>","PeriodicalId":53625,"journal":{"name":"Journal of Integrative Bioinformatics","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2021-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1515/jib-2020-0033","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"25393879","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A computational model for GPCR-ligand interaction prediction.","authors":"Shiva Karimi, Maryam Ahmadi, Farjam Goudarzi, Reza Ferdousi","doi":"10.1515/jib-2019-0084","DOIUrl":"10.1515/jib-2019-0084","url":null,"abstract":"<p><p>G protein-coupled receptors (GPCRs) play an essential role in critical human activities, and they are considered targets for a wide range of drugs. Accordingly, based on these crucial roles, GPCRs are mainly considered and focused on pharmaceutical research. Hence, there are a lot of investigations on GPCRs. Experimental laboratory research is very costly in terms of time and expenses, and accordingly, there is a marked tendency to use computational methods as an alternative method. In this study, a prediction model based on machine learning (ML) approaches was developed to predict GPCRs and ligand interactions. Decision tree (DT), random forest (RF), multilayer perceptron (MLP), support vector machine (SVM), and Naive Bayes (NB) were the algorithms that were investigated in this study. After several optimization steps, receiver operating characteristic (ROC) for DT, RF, MLP, SVM, and NB algorithm were 95.2, 98.1, 96.3, 95.5, and 97.3, respectively. Accordingly final model was made base on the RF algorithm. The current computational study compared with others focused on specific and important types of proteins (GPCR) interaction and employed/examined different types of sequence-based features to obtain more accurate results. Drug science researchers could widely use the developed prediction model in this study. The developed predictor was applied over 16,132 GPCR-ligand pairs and about 6778 potential interactions predicted.</p>","PeriodicalId":53625,"journal":{"name":"Journal of Integrative Bioinformatics","volume":null,"pages":null},"PeriodicalIF":1.5,"publicationDate":"2020-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7790179/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39108876","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A novel gene expression test method of minimizing breast cancer risk in reduced cost and time by improving SVM-RFE gene selection method combined with LASSO.","authors":"Madhuri Gupta,&nbsp;Bharat Gupta","doi":"10.1515/jib-2019-0110","DOIUrl":"https://doi.org/10.1515/jib-2019-0110","url":null,"abstract":"<p><p>Breast cancer is the leading diseases of death in women. It induces by a genetic mutation in breast cancer cells. Genetic testing has become popular to detect the mutation in genes but test cost is relatively expensive for several patients in developing countries like India. Genetic test takes between 2 and 4 weeks to decide the cancer. The time duration suffers the prognosis of genes because some patients have high rate of cancerous cell growth. In the research work, a cost and time efficient method is proposed to predict the gene expression level on the basis of clinical outcomes of the patient by using machine learning techniques. An improved SVM-RFE_MI gene selection technique is proposed to find the most significant genes related to breast cancer afterward explained variance statistical analysis is applied to extract the genes contain high variance. Least Absolute Shrinkage Selector Operator (LASSO) and Ridge regression techniques are used to predict the gene expression level. The proposed method predicts the expression of significant genes with reduced Root Mean Square Error and acceptable adjusted R-square value. As per the study, analysis of these selected genes is beneficial to diagnose the breast cancer at prior stage in reduced cost and time.</p>","PeriodicalId":53625,"journal":{"name":"Journal of Integrative Bioinformatics","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2020-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1515/jib-2019-0110","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39108875","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Cell – extracellular matrix interaction in glioma growth. I n silico model","authors":"V. Kalinin","doi":"10.1515/jib-2020-0027","DOIUrl":"https://doi.org/10.1515/jib-2020-0027","url":null,"abstract":"Abstract The study aims to investigate the role of viscoelastic interactions between cells and extracellular matrix (ECM) in avascular tumor growth. Computer simulations of glioma multicellular tumor spheroid (MTS) growth are being carried out for various conditions. The calculations are based on a continuous model, which simulates oxygen transport into MTS; transitions between three cell phenotypes, cell transport, conditioned by hydrostatic forces in cell–ECM composite system, cell motility and cell adhesion. Visco-elastic cell aggregation and elastic ECM scaffold represent two compressible constituents of the composite. Cell–ECM interactions form a Transition Layer on the spheroid surface, where mechanical characteristics of tumor undergo rapid transition. This layer facilitates tumor progression to a great extent. The study demonstrates strong effects of ECM stiffness, mechanical deformations of the matrix and cell–cell adhesion on tumor progression. The simulations show in particular that at certain, rather high degrees of matrix stiffness a formation of distant multicellular clusters takes place, while at further increase of ECM stiffness subtumors do not form. The model also illustrates to what extent mere mechanical properties of cell–ECM system may contribute into variations of glioma invasion scenarios.","PeriodicalId":53625,"journal":{"name":"Journal of Integrative Bioinformatics","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1515/jib-2020-0027","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45173423","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}